Overview

Dataset statistics

Number of variables12
Number of observations1275
Missing cells102
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.7 KiB
Average record size in memory96.1 B

Variable types

Categorical1
Numeric11

Alerts

STATE has a high cardinality: 51 distinct valuesHigh cardinality
ENROLL is highly overall correlated with TOTAL_REVENUE and 9 other fieldsHigh correlation
TOTAL_REVENUE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
FEDERAL_REVENUE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
STATE_REVENUE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
LOCAL_REVENUE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
TOTAL_EXPENDITURE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
INSTRUCTION_EXPENDITURE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
SUPPORT_SERVICES_EXPENDITURE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
OTHER_EXPENDITURE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
CAPITAL_OUTLAY_EXPENDITURE is highly overall correlated with ENROLL and 8 other fieldsHigh correlation
STATE is highly overall correlated with ENROLLHigh correlation
ENROLL has 51 (4.0%) missing valuesMissing
OTHER_EXPENDITURE has 51 (4.0%) missing valuesMissing
STATE is uniformly distributedUniform
FEDERAL_REVENUE has unique valuesUnique
LOCAL_REVENUE has unique valuesUnique
TOTAL_EXPENDITURE has unique valuesUnique
INSTRUCTION_EXPENDITURE has unique valuesUnique
SUPPORT_SERVICES_EXPENDITURE has unique valuesUnique
CAPITAL_OUTLAY_EXPENDITURE has unique valuesUnique
STATE_REVENUE has 25 (2.0%) zerosZeros

Reproduction

Analysis started2023-04-06 00:27:56.442072
Analysis finished2023-04-06 00:28:32.610171
Duration36.17 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

STATE
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM 

Distinct51
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Alabama
 
25
Pennsylvania
 
25
Nevada
 
25
New Hampshire
 
25
New Jersey
 
25
Other values (46)
1150 

Length

Max length20
Median length12
Mean length8.6666667
Min length4

Characters and Unicode

Total characters11050
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlaska
3rd rowArizona
4th rowArkansas
5th rowCalifornia

Common Values

ValueCountFrequency (%)
Alabama 25
 
2.0%
Pennsylvania 25
 
2.0%
Nevada 25
 
2.0%
New Hampshire 25
 
2.0%
New Jersey 25
 
2.0%
New Mexico 25
 
2.0%
New York 25
 
2.0%
North Carolina 25
 
2.0%
North Dakota 25
 
2.0%
Ohio 25
 
2.0%
Other values (41) 1025
80.4%

Length

2023-04-05T21:28:32.782171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 100
 
6.3%
north 50
 
3.2%
dakota 50
 
3.2%
virginia 50
 
3.2%
south 50
 
3.2%
carolina 50
 
3.2%
hawaii 25
 
1.6%
georgia 25
 
1.6%
florida 25
 
1.6%
columbia 25
 
1.6%
Other values (45) 1125
71.4%

Most occurring characters

ValueCountFrequency (%)
a 1450
13.1%
i 1050
 
9.5%
o 875
 
7.9%
n 875
 
7.9%
s 775
 
7.0%
e 700
 
6.3%
r 550
 
5.0%
t 475
 
4.3%
l 375
 
3.4%
h 325
 
2.9%
Other values (36) 3600
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9200
83.3%
Uppercase Letter 1550
 
14.0%
Space Separator 300
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1450
15.8%
i 1050
11.4%
o 875
9.5%
n 875
9.5%
s 775
8.4%
e 700
 
7.6%
r 550
 
6.0%
t 475
 
5.2%
l 375
 
4.1%
h 325
 
3.5%
Other values (14) 1750
19.0%
Uppercase Letter
ValueCountFrequency (%)
M 225
14.5%
N 200
12.9%
C 150
9.7%
I 125
 
8.1%
D 100
 
6.5%
W 100
 
6.5%
A 100
 
6.5%
O 75
 
4.8%
V 75
 
4.8%
T 50
 
3.2%
Other values (11) 350
22.6%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10750
97.3%
Common 300
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1450
13.5%
i 1050
 
9.8%
o 875
 
8.1%
n 875
 
8.1%
s 775
 
7.2%
e 700
 
6.5%
r 550
 
5.1%
t 475
 
4.4%
l 375
 
3.5%
h 325
 
3.0%
Other values (35) 3300
30.7%
Common
ValueCountFrequency (%)
300
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1450
13.1%
i 1050
 
9.5%
o 875
 
7.9%
n 875
 
7.9%
s 775
 
7.0%
e 700
 
6.3%
r 550
 
5.0%
t 475
 
4.3%
l 375
 
3.4%
h 325
 
2.9%
Other values (36) 3600
32.6%

YEAR
Real number (ℝ)

Distinct25
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004
Minimum1992
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:32.954171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q11998
median2004
Q32010
95-th percentile2015
Maximum2016
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2139321
Coefficient of variation (CV)0.0035997665
Kurtosis-1.2038598
Mean2004
Median Absolute Deviation (MAD)6
Skewness0
Sum2555100
Variance52.040816
MonotonicityIncreasing
2023-04-05T21:28:33.116200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1992 51
 
4.0%
2005 51
 
4.0%
2015 51
 
4.0%
2014 51
 
4.0%
2013 51
 
4.0%
2012 51
 
4.0%
2011 51
 
4.0%
2010 51
 
4.0%
2009 51
 
4.0%
2008 51
 
4.0%
Other values (15) 765
60.0%
ValueCountFrequency (%)
1992 51
4.0%
1993 51
4.0%
1994 51
4.0%
1995 51
4.0%
1996 51
4.0%
1997 51
4.0%
1998 51
4.0%
1999 51
4.0%
2000 51
4.0%
2001 51
4.0%
ValueCountFrequency (%)
2016 51
4.0%
2015 51
4.0%
2014 51
4.0%
2013 51
4.0%
2012 51
4.0%
2011 51
4.0%
2010 51
4.0%
2009 51
4.0%
2008 51
4.0%
2007 51
4.0%

ENROLL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1224
Distinct (%)100.0%
Missing51
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean917541.57
Minimum43866
Maximum6307022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:33.290023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum43866
5-th percentile96514.95
Q1264514.5
median649933.5
Q31010532
95-th percentile2742849
Maximum6307022
Range6263156
Interquartile range (IQR)746017.5

Descriptive statistics

Standard deviation1066514.2
Coefficient of variation (CV)1.1623606
Kurtosis9.9233815
Mean917541.57
Median Absolute Deviation (MAD)376180
Skewness2.8857218
Sum1.1230709 × 109
Variance1.1374525 × 1012
MonotonicityNot monotonic
2023-04-05T21:28:33.466040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281735 1
 
0.1%
6234155 1
 
0.1%
474423 1
 
0.1%
981303 1
 
0.1%
130236 1
 
0.1%
745668 1
 
0.1%
85991 1
 
0.1%
867929 1
 
0.1%
1029576 1
 
0.1%
893807 1
 
0.1%
Other values (1214) 1214
95.2%
(Missing) 51
 
4.0%
ValueCountFrequency (%)
43866 1
0.1%
44179 1
0.1%
44199 1
0.1%
44331 1
0.1%
44618 1
0.1%
44942 1
0.1%
46155 1
0.1%
48336 1
0.1%
56943 1
0.1%
58191 1
0.1%
ValueCountFrequency (%)
6307022 1
0.1%
6295994 1
0.1%
6288686 1
0.1%
6284502 1
0.1%
6258421 1
0.1%
6234155 1
0.1%
6226552 1
0.1%
6226523 1
0.1%
6224685 1
0.1%
6217031 1
0.1%

TOTAL_REVENUE
Real number (ℝ)

Distinct1274
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9102044.9
Minimum465650
Maximum89217262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:33.670027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum465650
5-th percentile941710.9
Q12189503.5
median5085826
Q310845163
95-th percentile29628556
Maximum89217262
Range88751612
Interquartile range (IQR)8655659.5

Descriptive statistics

Standard deviation11759620
Coefficient of variation (CV)1.2919756
Kurtosis11.090841
Mean9102044.9
Median Absolute Deviation (MAD)3398671
Skewness3.0159389
Sum1.1605107 × 1010
Variance1.3828867 × 1014
MonotonicityNot monotonic
2023-04-05T21:28:33.858837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3870164 2
 
0.2%
2678885 1
 
0.1%
3569311 1
 
0.1%
26357235 1
 
0.1%
6107684 1
 
0.1%
5780151 1
 
0.1%
22746589 1
 
0.1%
1104803 1
 
0.1%
13610326 1
 
0.1%
53147343 1
 
0.1%
Other values (1264) 1264
99.1%
ValueCountFrequency (%)
465650 1
0.1%
507927 1
0.1%
551668 1
0.1%
559481 1
0.1%
580148 1
0.1%
603902 1
0.1%
615130 1
0.1%
629748 1
0.1%
634237 1
0.1%
642234 1
0.1%
ValueCountFrequency (%)
89217262 1
0.1%
78248042 1
0.1%
74626928 1
0.1%
73958896 1
0.1%
72516936 1
0.1%
72389126 1
0.1%
70953331 1
0.1%
69016392 1
0.1%
68868516 1
0.1%
67356719 1
0.1%

FEDERAL_REVENUE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean767779.86
Minimum31020
Maximum9990221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:34.054877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum31020
5-th percentile69489
Q1189957.5
median403548
Q3827932
95-th percentile2600104.8
Maximum9990221
Range9959201
Interquartile range (IQR)637974.5

Descriptive statistics

Standard deviation1146991.5
Coefficient of variation (CV)1.4939067
Kurtosis21.837863
Mean767779.86
Median Absolute Deviation (MAD)268845
Skewness4.1511723
Sum9.7891933 × 108
Variance1.3155895 × 1012
MonotonicityNot monotonic
2023-04-05T21:28:34.250878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304177 1
 
0.1%
467492 1
 
0.1%
1868700 1
 
0.1%
515223 1
 
0.1%
642088 1
 
0.1%
1523431 1
 
0.1%
145770 1
 
0.1%
1240410 1
 
0.1%
3129776 1
 
0.1%
976009 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
31020 1
0.1%
31714 1
0.1%
31757 1
0.1%
33672 1
0.1%
34179 1
0.1%
34680 1
0.1%
34989 1
0.1%
35162 1
0.1%
35437 1
0.1%
36693 1
0.1%
ValueCountFrequency (%)
9990221 1
0.1%
9745250 1
0.1%
9715980 1
0.1%
8793325 1
0.1%
7984614 1
0.1%
7836263 1
0.1%
7818075 1
0.1%
7709079 1
0.1%
7556365 1
0.1%
7421482 1
0.1%

STATE_REVENUE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1251
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4223742.9
Minimum0
Maximum50904567
Zeros25
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:34.450842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile315896.7
Q11165776
median2537754
Q35055547.5
95-th percentile11609792
Maximum50904567
Range50904567
Interquartile range (IQR)3889771.5

Descriptive statistics

Standard deviation5549734.8
Coefficient of variation (CV)1.3139376
Kurtosis18.877029
Mean4223742.9
Median Absolute Deviation (MAD)1596765
Skewness3.7717233
Sum5.3852722 × 109
Variance3.0799557 × 1013
MonotonicityNot monotonic
2023-04-05T21:28:34.686875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.0%
1659028 1
 
0.1%
2542639 1
 
0.1%
9032615 1
 
0.1%
3200001 1
 
0.1%
2957101 1
 
0.1%
9777048 1
 
0.1%
383307 1
 
0.1%
8009636 1
 
0.1%
24036865 1
 
0.1%
Other values (1241) 1241
97.3%
ValueCountFrequency (%)
0 25
2.0%
83613 1
 
0.1%
84342 1
 
0.1%
84837 1
 
0.1%
86296 1
 
0.1%
89556 1
 
0.1%
94550 1
 
0.1%
120851 1
 
0.1%
126591 1
 
0.1%
127621 1
 
0.1%
ValueCountFrequency (%)
50904567 1
0.1%
43187637 1
0.1%
42360470 1
0.1%
42333637 1
0.1%
40084244 1
0.1%
38410554 1
0.1%
37752712 1
0.1%
37439651 1
0.1%
36413273 1
0.1%
35141208 1
0.1%

LOCAL_REVENUE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4110522.2
Minimum22093
Maximum36105265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:34.886870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum22093
5-th percentile296319.5
Q1715121
median2058996
Q34755293
95-th percentile15828045
Maximum36105265
Range36083172
Interquartile range (IQR)4040172

Descriptive statistics

Standard deviation5489562
Coefficient of variation (CV)1.3354902
Kurtosis7.3981155
Mean4110522.2
Median Absolute Deviation (MAD)1490812
Skewness2.5518737
Sum5.2409157 × 109
Variance3.0135291 × 1013
MonotonicityNot monotonic
2023-04-05T21:28:35.075621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
715680 1
 
0.1%
559180 1
 
0.1%
15455920 1
 
0.1%
2392460 1
 
0.1%
2180962 1
 
0.1%
11446110 1
 
0.1%
575726 1
 
0.1%
4360280 1
 
0.1%
25980702 1
 
0.1%
14580349 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
22093 1
0.1%
23917 1
0.1%
28531 1
0.1%
28736 1
0.1%
29587 1
0.1%
30061 1
0.1%
30143 1
0.1%
30597 1
0.1%
30975 1
0.1%
31085 1
0.1%
ValueCountFrequency (%)
36105265 1
0.1%
34941513 1
0.1%
33158857 1
0.1%
32655563 1
0.1%
31897154 1
0.1%
30603616 1
0.1%
30326508 1
0.1%
29447297 1
0.1%
28783628 1
0.1%
28647823 1
0.1%

TOTAL_EXPENDITURE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9206242.1
Minimum481665
Maximum85320133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:35.511626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum481665
5-th percentile952919.7
Q12170404
median5242672
Q310744202
95-th percentile29738419
Maximum85320133
Range84838468
Interquartile range (IQR)8573797.5

Descriptive statistics

Standard deviation11992791
Coefficient of variation (CV)1.3026804
Kurtosis10.861663
Mean9206242.1
Median Absolute Deviation (MAD)3517695
Skewness3.0155835
Sum1.1737959 × 1010
Variance1.4382704 × 1014
MonotonicityNot monotonic
2023-04-05T21:28:35.697642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2653798 1
 
0.1%
3599724 1
 
0.1%
26597713 1
 
0.1%
6407512 1
 
0.1%
5667749 1
 
0.1%
21951104 1
 
0.1%
1085970 1
 
0.1%
13731201 1
 
0.1%
54870811 1
 
0.1%
26168480 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
481665 1
0.1%
525289 1
0.1%
553732 1
0.1%
588983 1
0.1%
593126 1
0.1%
614807 1
0.1%
621313 1
0.1%
621904 1
0.1%
623146 1
0.1%
624941 1
0.1%
ValueCountFrequency (%)
85320133 1
0.1%
78365958 1
0.1%
76618865 1
0.1%
74766086 1
0.1%
73225422 1
0.1%
72506810 1
0.1%
71227111 1
0.1%
70191877 1
0.1%
69847705 1
0.1%
69727119 1
0.1%

INSTRUCTION_EXPENDITURE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4768010.5
Minimum265549
Maximum43964520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:35.897647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum265549
5-th percentile480588.5
Q11171336.5
median2658253
Q35561959
95-th percentile15856875
Maximum43964520
Range43698971
Interquartile range (IQR)4390622.5

Descriptive statistics

Standard deviation6300569.3
Coefficient of variation (CV)1.3214252
Kurtosis11.690894
Mean4768010.5
Median Absolute Deviation (MAD)1767647
Skewness3.1224985
Sum6.0792134 × 109
Variance3.9697174 × 1013
MonotonicityNot monotonic
2023-04-05T21:28:36.087589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1481703 1
 
0.1%
1732652 1
 
0.1%
12375013 1
 
0.1%
3217111 1
 
0.1%
2773416 1
 
0.1%
10494471 1
 
0.1%
552250 1
 
0.1%
7229214 1
 
0.1%
33446544 1
 
0.1%
13739017 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
265549 1
0.1%
283484 1
0.1%
284638 1
0.1%
310940 1
0.1%
314138 1
0.1%
315009 1
0.1%
318260 1
0.1%
319014 1
0.1%
320561 1
0.1%
327382 1
0.1%
ValueCountFrequency (%)
43964520 1
0.1%
42587272 1
0.1%
41954260 1
0.1%
40168920 1
0.1%
38951948 1
0.1%
38756656 1
0.1%
38431932 1
0.1%
37834196 1
0.1%
36628097 1
0.1%
36542347 1
0.1%

SUPPORT_SERVICES_EXPENDITURE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2682586.5
Minimum139963
Maximum26058021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:36.283628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum139963
5-th percentile283829.4
Q1638076
median1525471
Q33222924.5
95-th percentile8986370.1
Maximum26058021
Range25918058
Interquartile range (IQR)2584848.5

Descriptive statistics

Standard deviation3357214.5
Coefficient of variation (CV)1.2514841
Kurtosis10.665868
Mean2682586.5
Median Absolute Deviation (MAD)1036456
Skewness2.8870881
Sum3.4202978 × 109
Variance1.1270889 × 1013
MonotonicityNot monotonic
2023-04-05T21:28:36.496022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
735036 1
 
0.1%
1129277 1
 
0.1%
7655651 1
 
0.1%
2061901 1
 
0.1%
1823162 1
 
0.1%
7164617 1
 
0.1%
300054 1
 
0.1%
3566002 1
 
0.1%
13610428 1
 
0.1%
8737387 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
139963 1
0.1%
148152 1
0.1%
152340 1
0.1%
156042 1
0.1%
158223 1
0.1%
159739 1
0.1%
169545 1
0.1%
169697 1
0.1%
177522 1
0.1%
184916 1
0.1%
ValueCountFrequency (%)
26058021 1
0.1%
23501848 1
0.1%
22310865 1
0.1%
21693675 1
0.1%
21594573 1
0.1%
20704755 1
0.1%
20655194 1
0.1%
20595508 1
0.1%
20393761 1
0.1%
20266047 1
0.1%

OTHER_EXPENDITURE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1222
Distinct (%)99.8%
Missing51
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean429950.93
Minimum11541
Maximum3995951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:36.711016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11541
5-th percentile38708.2
Q1103449.25
median271704
Q3517222.25
95-th percentile1370407.6
Maximum3995951
Range3984410
Interquartile range (IQR)413773

Descriptive statistics

Standard deviation534789.31
Coefficient of variation (CV)1.243838
Kurtosis13.560006
Mean429950.93
Median Absolute Deviation (MAD)191753
Skewness3.2260361
Sum5.2625994 × 108
Variance2.8599961 × 1011
MonotonicityNot monotonic
2023-04-05T21:28:36.923027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
490641 2
 
0.2%
70608 2
 
0.2%
456737 1
 
0.1%
490336 1
 
0.1%
455403 1
 
0.1%
70835 1
 
0.1%
561019 1
 
0.1%
43352 1
 
0.1%
451721 1
 
0.1%
190634 1
 
0.1%
Other values (1212) 1212
95.1%
(Missing) 51
 
4.0%
ValueCountFrequency (%)
11541 1
0.1%
14152 1
0.1%
21047 1
0.1%
21161 1
0.1%
21910 1
0.1%
21975 1
0.1%
22308 1
0.1%
22636 1
0.1%
22698 1
0.1%
23104 1
0.1%
ValueCountFrequency (%)
3995951 1
0.1%
3759373 1
0.1%
3754315 1
0.1%
3692956 1
0.1%
3581789 1
0.1%
3556156 1
0.1%
3488344 1
0.1%
3486160 1
0.1%
3467540 1
0.1%
3461759 1
0.1%

CAPITAL_OUTLAY_EXPENDITURE
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean903467.45
Minimum12708
Maximum10223657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-04-05T21:28:37.143050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum12708
5-th percentile52981.6
Q1181507
median510428
Q3966148
95-th percentile3026542
Maximum10223657
Range10210949
Interquartile range (IQR)784641

Descriptive statistics

Standard deviation1329473.5
Coefficient of variation (CV)1.4715234
Kurtosis15.854028
Mean903467.45
Median Absolute Deviation (MAD)364393
Skewness3.6071538
Sum1.151921 × 109
Variance1.7674997 × 1012
MonotonicityNot monotonic
2023-04-05T21:28:37.352793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174053 1
 
0.1%
569550 1
 
0.1%
2537699 1
 
0.1%
621540 1
 
0.1%
459000 1
 
0.1%
2568654 1
 
0.1%
88292 1
 
0.1%
1728313 1
 
0.1%
4805621 1
 
0.1%
1625458 1
 
0.1%
Other values (1265) 1265
99.2%
ValueCountFrequency (%)
12708 1
0.1%
14685 1
0.1%
14866 1
0.1%
15452 1
0.1%
15655 1
0.1%
18871 1
0.1%
20070 1
0.1%
20181 1
0.1%
20234 1
0.1%
21853 1
0.1%
ValueCountFrequency (%)
10223657 1
0.1%
10058842 1
0.1%
10047331 1
0.1%
9388155 1
0.1%
9297597 1
0.1%
9242549 1
0.1%
8881265 1
0.1%
8332612 1
0.1%
8133118 1
0.1%
8055742 1
0.1%

Interactions

2023-04-05T21:28:29.539530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:27:58.074704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:03.330626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:06.896492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:09.692490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:12.704482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:16.205891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:19.426139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:22.070136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:24.686131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:27.167557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:29.731731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:27:58.545446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:03.678622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:07.156489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:09.964485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:12.992483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:16.589890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:19.654138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:22.326136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:24.918131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:27.367571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:29.939726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:27:58.977290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:04.174623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:07.372488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:10.196523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:13.224484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:16.913890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:19.898136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:22.578134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:25.114131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:27.583564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:30.127725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:27:59.499891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:04.528494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:07.624487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:10.420486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:13.596481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:17.141890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:20.102145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:22.822134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:25.322130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:27.783535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:30.339791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:00.014514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:04.868492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:07.848487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:10.672487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:14.228140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:17.401889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:20.298135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:23.066136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:25.590129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:27.995532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:30.563789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:00.453341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:05.208492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:08.140486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:10.976483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:14.544142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:17.785901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:20.506136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:23.242132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:25.778132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:28.223531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:30.771793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:00.785455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:05.500490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:08.396487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:11.268485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:14.788140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:18.101889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:20.778137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:23.450133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:25.978129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:28.463531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:30.955806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:01.177405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:05.728491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:08.612488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:11.500483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:15.020142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:18.321888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:21.038135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:23.694132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:26.359534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:28.687533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:31.155829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:01.458653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:06.060492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:08.808517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:11.716483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:15.236138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:18.601887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:21.266139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:23.974134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:26.563536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:28.891531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:31.367805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:02.258512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:06.292494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:09.016486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:12.068482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:15.536138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:18.937886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:21.478135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:24.178130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:26.751535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:29.099531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:31.619826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:02.774134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:06.592528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:09.232487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:12.408484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:15.871712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:19.189888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:21.790137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:24.402132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:26.963537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2023-04-05T21:28:29.323533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2023-04-05T21:28:37.540799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
YEARENROLLTOTAL_REVENUEFEDERAL_REVENUESTATE_REVENUELOCAL_REVENUETOTAL_EXPENDITUREINSTRUCTION_EXPENDITURESUPPORT_SERVICES_EXPENDITUREOTHER_EXPENDITURECAPITAL_OUTLAY_EXPENDITURESTATE
YEAR1.0000.0290.2910.4200.2980.2320.2860.2770.3060.2410.2470.000
ENROLL0.0291.0000.9470.8730.9280.9080.9480.9490.9380.9400.9030.737
TOTAL_REVENUE0.2910.9471.0000.9320.9770.9540.9990.9980.9960.9610.9310.423
FEDERAL_REVENUE0.4200.8730.9321.0000.9280.8490.9310.9260.9370.9340.8850.353
STATE_REVENUE0.2980.9280.9770.9281.0000.8780.9770.9730.9730.9590.9230.426
LOCAL_REVENUE0.2320.9080.9540.8490.8781.0000.9540.9560.9500.8930.8810.435
TOTAL_EXPENDITURE0.2860.9480.9990.9310.9770.9541.0000.9980.9960.9610.9370.458
INSTRUCTION_EXPENDITURE0.2770.9490.9980.9260.9730.9560.9981.0000.9930.9590.9220.434
SUPPORT_SERVICES_EXPENDITURE0.3060.9380.9960.9370.9730.9500.9960.9931.0000.9540.9240.439
OTHER_EXPENDITURE0.2410.9400.9610.9340.9590.8930.9610.9590.9541.0000.9070.475
CAPITAL_OUTLAY_EXPENDITURE0.2470.9030.9310.8850.9230.8810.9370.9220.9240.9071.0000.403
STATE0.0000.7370.4230.3530.4260.4350.4580.4340.4390.4750.4031.000

Missing values

2023-04-05T21:28:31.915789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-05T21:28:32.239332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-05T21:28:32.498171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

STATEYEARENROLLTOTAL_REVENUEFEDERAL_REVENUESTATE_REVENUELOCAL_REVENUETOTAL_EXPENDITUREINSTRUCTION_EXPENDITURESUPPORT_SERVICES_EXPENDITUREOTHER_EXPENDITURECAPITAL_OUTLAY_EXPENDITURE
0Alabama1992NaN2678885304177165902871568026537981481703735036NaN174053
1Alaska1992NaN1049591106780720711222100972488498362350902NaN37451
2Arizona1992NaN325807929788813698151590376340158014359081007732NaN609114
3Arkansas1992NaN17119591785719587855746031743022964323483488NaN145212
4California1992NaN26260025207247016546514764104127138832143589228520926NaN2044688
5Colorado1992NaN318517316325313079861713934326482616424661035970NaN364760
6Connecticut1992NaN383430214354213425392348221372133821480411142600NaN48542
7Delaware1992NaN64523345945420942178346638784372722194915NaN30595
8District of Columbia1992NaN709480647490644731742893329160316679NaN47272
9Florida1992NaN11506299788420568394950339301130564251663743410440NaN1667826
STATEYEARENROLLTOTAL_REVENUEFEDERAL_REVENUESTATE_REVENUELOCAL_REVENUETOTAL_EXPENDITUREINSTRUCTION_EXPENDITURESUPPORT_SERVICES_EXPENDITUREOTHER_EXPENDITURECAPITAL_OUTLAY_EXPENDITURE
1265South Dakota2016134045.01455737196644438629820464147586473017644058078149.0192504
1266Tennessee2016999265.09585331109541543957574094159968051254018122915193569611.0553997
1267Texas20165053291.058284155595334822004299303265085931488427862199151690072855527.08332612
1268Utah2016580215.0495292339406025335732025290492365725916001217100286744.0707988
1269Vermont201687974.0211236511189116145183859562082696101320958317856289.042911
1270Virginia20161283493.0162592741058146629758789035411649752089446145164699643506.01161323
1271Washington20161083973.0149643641097684921120146554791525329670873654904575577606.02204059
1272West Virginia2016276764.0339157935806818630621170449336656618042351127255236487.0181392
1273Wisconsin2016857736.011697466782610598676349280931178753557604183691439508013.01082849
1274Wyoming201694511.020446691230121175899745758203422992149458570053570.0454302